Majed Chambah

Perceptual approach for unsupervised digital color restoration of cinematographic archives

The cinematographic archives represent an important part of our collective memory. We present in this paper some advances in automating the color fading restoration process, especially with regard to the automatic color correction technique. The proposed color correction method is based on the ACE model, an unsupervised color equalization algorithm based on a perceptual approach and inspired by some adaptation mechanisms of the human visual system, in particular lightness constancy and color constancy. There are some advantages in a perceptual approach: mainly its robustness and its local filtering properties, that lead to more effective results. The resulting technique, is not just an application of ACE on movie images, but an enhancement of ACE principles to meet the requirements in the digital film restoration field. The presented preliminary results are satisfying and promising.

Recent progress in automatic digital restoration of color motion pictures

The motion pictures represent a precious cultural heritage, however the chemical support on which they are recorded becomes unstable with time, unless they are stored at low temperatures. Some defects affecting color movies, such as bleaching, are out of reach of photochemical restoration means, digital restoration is hence unquestionable. We propose an original automatic technique for faded image correction. Bleaching results in damage to one or two chromatic layers, giving a drab image with poor saturation and an overall color cast. Our automatic fading correction technique consists in reviving the colors of the image (color enhancement), then in balancing the colors of the image.

Are Existing Procedures Enough? Image and Video Quality Assessment: Review of Subjective and Objective Metrics

Images and videos are subject to a wide variety of distortions during acquisition, digitizing, processing, restoration, compression, storage, transmission and reproduction, any of which may result in degradation in visual quality. That is why image quality assessment plays a major role in many image processing applications. Image and video quality metrics can be classified by using a number of criteria such as the type of the application domain, the predicted distortion (noise, blur, etc.) and the type of information needed to assess the quality (original image, distorted image, etc.). In the literature, the most reliable way of assessing the quality of an image or of a video is subjective evaluation [1], because human beings are the ultimate receivers in most applications. The subjective quality metric, obtained from a number of human observers, has been regarded for many years as the most reliable form of quality measurement. However, this approach is too cumbersome, slow and expensive for most applications [2]. So, in recent years a great effort has been made towards the development of quantitative measures. The objective quality evaluation is automated, done in real time and needs no user interaction. But ideally, such a quality assessment system would perceive and measure image or video impairments just like a human being [3]. The quality assessment is so important and is still an active and evolving research topic because it is a central issue in the design, implementation, and performance testing of all systems [4, 5]. Usually, the relevant literature and the related work present only a state of the art of metrics that are limited to a specific application domain. The major goal of this paper is to present a wider state of the art of the most used metrics in several application domains such as compression [6], restoration [7], etc. In this paper, we review the basic concepts and methods in subjective and objective image/video quality assessment research and we discuss their performances and drawbacks in each application domain. We show that if in some domains a lot of work has been done and several metrics were developed, on the other hand, in some other domains a lot of work has to be done and specific metrics need to be developed.

Tuning of perceptual technique for digital movie color restoration

In this paper we present tests and results of an automatic color fading restoration process for digitized movies. The proposed color correction method is based on the ACE model, an unsupervised color equalization algorithm based on a perceptual approach and inspired by some mechanisms of the human visual system. This perceptual approach is local, robust and does not need any user region selection or any other user supervision. However the model has a small number of parameters that has to be set once before the filtering. The tests presented in this paper aim to study these parameters and find their effect on the final result.

Image quality evaluation in the field of digital film restoration

Digital film restoration is a significant hope for cinematographic archivists. Technical progress, more powerful machines at lower cost, makes it possible nowadays to restore cinematographic archives digitally at acceptable paces. Several digital restoration techniques have emerged during the last decade and became more and more automated but restoration evaluation remains still a rarely tackled issue. After presenting the several defects than can affect cinematographic material, and the film digital restoration field, we present in this paper the issues of image quality evaluation in the field of digital film restoration and suggest some reference free objective measures.

Linear techniques for image sequence processing acceleration

In this paper we present and compare two linear techniques for image sequence enhancement speed up that transform image contrast and color considering mainly the spatial relationship between the image areas. The first technique called LLL for Linear Local LUT is a local technique based on a Look Up Table transformation. The second technique (called PC2D) is a global technique based on a color mapping between some key zones of the original and corrected image. The need for speed up technique is especially important when processing high definition images and live videos. To test and compare the performance of the two proposed methods we have chosen the ACE (Automatic Color Equalization) technique, an unsupervised color equalization algorithm. We applied the techniques to the fields of digital cinema and digital film restoration (images with high definition) and underwater aquarium videos (live videos).